code: def convert_sklearn_lgbm_classifier(operator, device, extra_config):
      """
      Converter for `lightgbm.LGBMClassifier` (trained using the Sklearn API).
  
      Args:
          operator: An operator wrapping a `lightgbm.LGBMClassifier` model
          device: String defining the type of device the converted operator should be run on
          extra_config: Extra configuration used to select the best conversion strategy
  
      Returns:
          A PyTorch model
      """
      assert operator is not None, "Cannot convert None operator"
      if operator.raw_operator.boosting_type == "rf":
          raise RuntimeError(
              "Unable to directly convert this model. "
              "It should be converted into ONNX first."
          )
  
      n_features = operator.raw_operator._n_features
      tree_infos = operator.raw_operator.booster_.dump_model()[
          "tree_info"
      ]
      n_classes = operator.raw_operator._n_classes
  
      return convert_gbdt_classifier_common(
          operator,
          tree_infos,
          _get_tree_parameters,
          n_features,
          n_classes,
          extra_config=extra_config,
      )
  
file_path: hummingbird/hummingbird/ml/operator_converters/lightgbm.py
imports: import numpy
  from onnxconverter_common.registration import register_converter
  from .None import constants
  from ._gbdt_commons import convert_gbdt_classifier_common, convert_gbdt_common
  from ._tree_commons import TreeParameters
label_desc: Use image node_id 3 for calling a global function with example usage: convert_sklearn_lgbm_classifier(operator, device, extra_config) and returns: convert_gbdt_classifier_common